UbiTrack: A Layout-Robust Neural System for Device-Free WiFi Tracking

Neural network-based device-free Wi-Fi tracking achieves superior accuracy over theory-driven approaches, yet its practical utility is severely limited by poor generalization across different device layouts. Existing models implicitly learn site-specific correlations, necessitating labor-intensive data recollection and retraining whenever the device configuration changes. To overcome this dependency, we propose UbiTrack , a generalized framework capable of robust tracking across diverse device layouts. Our core innovation lies in explicitly modeling the geometric constraints between device positions and signal propagation within the learning process. Instead of treating the layout as a black box, we formulate the tracking problem as a layout-aware mapping. We design a novel neural network architecture that incorporates geometric graph constraints into the analysis of temporal signal features, enabling the model to learn the intrinsic physical interactions between device topology and user trajectories. To facilitate the training of this data-hungry framework, we employ a progressive training strategy leveraging synthetic data derived from theory-driven principles. Extensive real-world experiments using commercial off-the-shelf (COTS) Wi-Fi devices validate that UbiTrack effectively decouples tracking performance from environmental setups. The system achieves median tracking errors of 0.56m in fixed layouts and 0.39m in variable layouts, paving the way for scalable and robust wireless sensing.